
# Import packages
import pandas as pd
import numpy as np
import os

BATH_PATH = os.getcwd()
ETC_PATH = os.path.join(BATH_PATH, "..", "etc")
DATA_PATH = os.path.join(BATH_PATH, "..", "data")

Meta_table_file = os.path.join(ETC_PATH, "rtem_all_points_metadata.csv")

# Valid Building Data
# 247
# 354
# 383
# 394
# 438


# Read Meta Table
Meta_table_DF = pd.read_csv(Meta_table_file)
Meta_table_DF.head()


# Extract Necessary meta of building
def extract_necessary_meta(building_id):
    # Preprocess
    Meta_table_DF = pd.read_csv(Meta_table_file)
    Meta_table_DF.loc[:, "units"] = np.where(Meta_table_DF.loc[:, "units"].isna(), Meta_table_DF.loc[:, "tagged_units"], Meta_table_DF.loc[:, "units"])
    Meta_table_DF.loc[:, "name_point"] = np.where(Meta_table_DF.loc[:, "name_point"].isna(), Meta_table_DF.loc[:, "description"], Meta_table_DF.loc[:, "name_point"])
    # Select columns/building_id
    select_cols = ["id_point", "building_id", "name_point", "description", "units", "tagged_units", "equip_id", "equip_type_abbr", "equip_type_name", "tags"]
    Meta_table_DF = Meta_table_DF[Meta_table_DF["building_id"]==building_id][select_cols]
    return Meta_table_DF

building_id = 488
extract_necessary_meta(building_id)


# Save meta for building
def save_meta_building(building_id):
    DF = extract_necessary_meta(building_id)
    DF.reset_index(drop=True, inplace=True)
    try:
        DF.to_csv(os.path.join(DATA_PATH, F"Building_{building_id}", F"Meta_Building_{building_id}.csv"))
        return print(F"Meta_Building_{building_id}.csv saved!")
    except:
        return print(F"Meta_Building_{building_id}.csv saving failed!")

building_id = 488
save_meta_building(building_id)

Meta_Building_488.csv saved!


# Check Building Numbers in DATA_PATH & Save meta for buildings
Building_Number_List = [int(n.split("_")[1]) for n in os.listdir(DATA_PATH)]
for bid in Building_Number_List:
    save_meta_building(bid)

Meta_Building_103.csv saved!
Meta_Building_119.csv saved!
Meta_Building_120.csv saved!
Meta_Building_141.csv saved!
Meta_Building_194.csv saved!
Meta_Building_247.csv saved!
Meta_Building_259.csv saved!
Meta_Building_332.csv saved!
Meta_Building_354.csv saved!
Meta_Building_383.csv saved!
Meta_Building_387.csv saved!
Meta_Building_393.csv saved!
Meta_Building_394.csv saved!
Meta_Building_418.csv saved!
Meta_Building_438.csv saved!
Meta_Building_450.csv saved!
Meta_Building_484.csv saved!
Meta_Building_485.csv saved!
Meta_Building_488.csv saved!
Meta_Building_503.csv saved!
Meta_Building_98.csv saved!


# Merge Building Data
def merge_building_data(building_id):
    Building_Data_path = os.path.join(DATA_PATH, F"Building_{building_id}")
    Building_Data_List = [device for device in os.listdir(Building_Data_path) if "rtem_API_data" in device]
    data_list = []
    for device in Building_Data_List:
        temp_data = pd.read_csv(os.path.join(DATA_PATH, F"Building_{building_id}", device))
        temp_data.set_index("timestamp", inplace=True)
        data_list.append(temp_data)
    res_DF = pd.concat(data_list, axis=1, join="outer")
    res_DF.index = pd.to_datetime(res_DF.index)
    for col in res_DF:
        res_DF.loc[:, col] = pd.to_numeric(res_DF.loc[:, col])
    return res_DF

building_id = 438
merged_data = merge_building_data(building_id)
display(merged_data)


# Select columns and standardize column names
def standardize_column_names(building_id):
    Building_Data_path = os.path.join(DATA_PATH, F"Building_{building_id}")
    # Point_Table
    Point_table_file = [point_table for point_table in os.listdir(Building_Data_path) if "Point_Table" in point_table]
    Point_table = pd.read_excel(os.path.join(DATA_PATH, F"Building_{building_id}", Point_table_file[0]))
    Point_table.sort_values(by="Point_ID", inplace=True)
    select_cols = [str(col) for col in Point_table.loc[:, "Point_ID"]]
    # Process data
    data = merge_building_data(building_id).loc[:, select_cols]
    data.columns = list(Point_table.loc[:, "Standardized_Point_Name"])
    for idx in Point_table.index:
        if (Point_table.loc[idx, "Unit"] == "F") or (Point_table.loc[idx, "Unit"] == "f"):
            data.loc[:, Point_table.loc[idx, "Standardized_Point_Name"]] = (data.loc[:, Point_table.loc[idx, "Standardized_Point_Name"]].values - 32) * 5 / 9
        if (Point_table.loc[idx, "Unit"] == "GPM") or (Point_table.loc[idx, "Unit"] == "gpm"): # GPM to LPH
            data.loc[:, Point_table.loc[idx, "Standardized_Point_Name"]] = (data.loc[:, Point_table.loc[idx, "Standardized_Point_Name"]].values) * 227.1
    return data
    
building_id = 438
merged_data = standardize_column_names(building_id)
display(merged_data.head(5))

OUTPUT_DATA_PATH = os.path.join(BATH_PATH, "..", "data", F"Building_{building_id}", "output_data")
merged_data.to_csv(os.path.join(OUTPUT_DATA_PATH, F"merged_data_{building_id}.csv"))
print(F"merged_data_{building_id} data saved!")

merged_data_438 data saved!


# Split Building Device 
def split_building_device(building_id):
    Building_Data_path = os.path.join(DATA_PATH, F"Building_{building_id}")
    Point_table_file = [point_table for point_table in os.listdir(Building_Data_path) if "Point_Table" in point_table]
    Device_Table = pd.read_excel(os.path.join(DATA_PATH, F"Building_{building_id}", Point_table_file[0]), sheet_name="Device_class")
    Device_Table["Keywords"] = [keyword.replace(";", "|") for keyword in Device_Table["Keywords"]]
    data = standardize_column_names(building_id)
    # Classify Points by Device_Name
    Chiller_dictionary = {}
    filter_Table = pd.DataFrame.from_dict({"TCHD_cal":[0, 10], "RT_cal":[0, 50000], "CH_KW":[0, 50000], "KWRT_cal":[0, 3], "COP_cal":[0, 10]}, orient="index", columns=["Filter_bottom", "Filter_top"])
    for idx in Device_Table.index:
        chiller_name = Device_Table.loc[idx, "Device_Name"]
        chiller_data_cols = data.columns.str.contains(Device_Table.loc[idx, "Keywords"], regex=True)
        chiller_data = data.loc[:, chiller_data_cols].copy()
        for col in chiller_data.columns:
            try:
                chiller_data.rename(columns={col: F'{col.split("-")[1]}'}, inplace=True)
            except:
                pass
        chiller_data.drop(["CH_KWH"], axis=1, inplace=True)
        # Process chiller_data
        TCHD = chiller_data.loc[:, "TCHR"].values - chiller_data.loc[:, "TCHS"].values
        RT_cal = chiller_data.loc[:, "QCH"].values * TCHD / 3024 # USRT
        # RT_cal = chiller_data.loc[:, "QCH"].values * TCHD * 1000/3600 * 4.2 / 3.516
        KWRT_cal = chiller_data.loc[:, "CH_KW"] / RT_cal
        COP_cal = 3.516 / KWRT_cal
        chiller_data.loc[:, "TCHD_cal"] = TCHD
        chiller_data.loc[:, "RT_cal"] = RT_cal
        chiller_data.loc[:, "KWRT_cal"] = KWRT_cal
        chiller_data.loc[:, "COP_cal"] = COP_cal
    
        for col in chiller_data.columns:
            if col in filter_Table.index:
                chiller_data.loc[:, col] = np.where(np.logical_or(chiller_data.loc[:, col]>filter_Table.loc[col, "Filter_top"], chiller_data.loc[:, col]<filter_Table.loc[col, "Filter_bottom"]), np.nan, chiller_data.loc[:, col])

        Chiller_dictionary[chiller_name] = chiller_data.dropna()
    return Chiller_dictionary

Chillers = split_building_device(building_id)
OUTPUT_DATA_PATH = os.path.join(BATH_PATH, "..", "data", F"Building_{building_id}", "output_data")

for chiller in Chillers.keys():
    temp_df = Chillers[chiller].head(5)
    display(temp_df.head(5))
    temp_df.to_csv(os.path.join(OUTPUT_DATA_PATH, F"{chiller}_data.csv"))
    print(F"{chiller} data saved!")

CH_22_3 data saved!

CH_22_4 data saved!


# Plot Figures
import plotly.graph_objects as go
import chart_studio.plotly as py
import cufflinks as cf
import seaborn as sns
import matplotlib.pyplot as plt
cf.go_offline()


# RT vs KWRT
Load_Data_List = []
for chiller in Chillers.keys():
    temp_df = Chillers[chiller]
    temp_df.loc[:, "Chiller_Name"] = chiller
    print(F"{chiller} data read: ")
    display(temp_df.head(5))
    Load_Data_List.append(temp_df)

    # Plot
    Fig = go.Figure()
    Fig.add_trace(go.Scatter(name="RT-KWRT", x=temp_df["RT_cal"], y=temp_df["KWRT_cal"], mode="markers"))
    Fig.update_layout(
        title = dict(text=F"Cooling Load vs KW/RT (Chiller: {chiller})", x=0.04, y=0.83),
        xaxis = dict(
            title=dict(text="Cooling Load (RT)", font=dict(size=12), standoff=0),
            showline=True, linewidth=1.2, linecolor="black",
            showticklabels=True, showgrid=True, gridcolor="rgba(230, 230, 230, 1)",
            rangeslider=dict(visible=False)),
        yaxis = dict(
            title=dict(text="Energy Performance (KW/RT)", font=dict(size=12), standoff=0),
            dtick=0.1,
            showline=True, linewidth=1.2, linecolor="black",
            zeroline=True, zerolinewidth=1.2, zerolinecolor="rgba(230, 230, 230, 1)",
            showgrid=True, gridcolor="rgba(230, 230, 230, 1)"),
        legend = dict(x=1.02, y=0.5, orientation="v", bordercolor="black", borderwidth=0.5, font=dict(size=8)),
        plot_bgcolor="white", width=700, height=400)
    Fig.show()

CH_22_3 data read:

CH_22_4 data read:


# Plot Timeseries & Analysis Table
Total_Load_cal = pd.concat(Load_Data_List, axis=1, join="outer")
Total_Load_cal.loc[:, "RT_total"] = Total_Load_cal["RT_cal"].sum(axis=1)
Load_Data_DF = pd.concat(Load_Data_List, axis=0)
Chiller_List = list(Load_Data_DF.loc[:, "Chiller_Name"].unique())
Fig = go.Figure()
for chiller in Chiller_List:
    Fig.add_trace(go.Scatter(name=chiller, x=Load_Data_DF[Load_Data_DF["Chiller_Name"]==chiller].index, y=Load_Data_DF[Load_Data_DF["Chiller_Name"]==chiller]["RT_cal"]))
Fig.add_trace(go.Scatter(name="RT_total", x=Total_Load_cal.index, y=Total_Load_cal["RT_total"]))
Fig.update_layout(
    title = dict(text=F"Cooling Load Timeseries", x=0.04, y=0.83),
    xaxis = dict(
        showline=True, linewidth=1.2, linecolor="black",
        showticklabels=True, showgrid=True, gridcolor="rgba(230, 230, 230, 1)",
        rangeslider=dict(visible=False)),
    yaxis = dict(
        title=dict(text="Cooling Load (RT)", font=dict(size=12), standoff=0),
        showline=True, linewidth=1.2, linecolor="black",
        zeroline=True, zerolinewidth=1.2, zerolinecolor="rgba(230, 230, 230, 1)",
        showgrid=True, gridcolor="rgba(230, 230, 230, 1)"),
    legend = dict(x=1.02, y=0.5, orientation="v", bordercolor="black", borderwidth=0.5, font=dict(size=8)),
    plot_bgcolor="white", width=700, height=400)
Fig.show()


# TCH Analysis
Load_Data_DF.reset_index(drop=False, inplace=True)
TCH_analysis = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["TCHS", "TCHR"]).describe().round(2)
print("Chilled Water Analysis")
display(TCH_analysis)

DF = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["TCHS", "TCHR"])
x_targ = DF.index
fig = go.Figure()
for Chiller_name, color_thick, color_thin in zip(Chiller_List, ["#2CBDFE", "#00cc99"], ["#80d6fe", "#b3ffec"]):
    try:
        y_targ1 = DF["TCHS"][Chiller_name]
        y_targ2 = DF["TCHR"][Chiller_name]
        fig.add_trace(go.Scatter(name=F"{Chiller_name}-TCHS", x=x_targ, y=y_targ1, mode="lines", line=dict(color=color_thick), showlegend=True))
        fig.add_trace(go.Scatter(name=F"{Chiller_name}-TCHR", x=x_targ, y=y_targ2, mode="lines", line=dict(color=color_thin), showlegend=True))
    except:
        print(F"{Chiller_name} Data is not sufficient to plot...\n")

fig.update_layout(
    title = dict(text=F"TCH Timeseries", x=0.04, y=0.83),
    xaxis = dict(
        showline=True, linewidth=1.2, linecolor="black",
        showticklabels=True, showgrid=True, gridcolor="rgba(230, 230, 230, 1)",
        rangeslider=dict(visible=False)),
    yaxis = dict(
        title=dict(text="Temp. (<sup>o</sup>C)", font=dict(size=12), standoff=0),
        showline=True, linewidth=1.2, linecolor="black",
        zeroline=True, zerolinewidth=1.2, zerolinecolor="rgba(230, 230, 230, 1)",
        showgrid=True, gridcolor="rgba(230, 230, 230, 1)"),
    legend = dict(x=1.02, y=0.5, orientation="v", bordercolor="black", borderwidth=0.5, font=dict(size=8)),
    plot_bgcolor="white", width=700, height=400)
fig.show()

Chilled Water Analysis


# QCH Analysis
Load_Data_DF.reset_index(drop=False, inplace=True)
QCH_analysis = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["QCH"]).describe().round(2)
print("QCH Analysis")
display(QCH_analysis)

DF = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["QCH"])
x_targ = DF.index
fig = go.Figure()
for Chiller_name, color_thick, color_thin in zip(Chiller_List, ["#2CBDFE", "#00cc99"], ["#80d6fe", "#b3ffec"]):
    try:
        y_targ1 = DF["QCH"][Chiller_name]
        fig.add_trace(go.Scatter(name=F"{Chiller_name}-QCH", x=x_targ, y=y_targ1, mode="lines", line=dict(color=color_thick), showlegend=True))
    except:
        print(F"{Chiller_name} Data is not sufficient to plot...\n")

fig.update_layout(
    title = dict(text=F"QCH Timeseries", x=0.04, y=0.83),
    xaxis = dict(
        showline=True, linewidth=1.2, linecolor="black",
        showticklabels=True, showgrid=True, gridcolor="rgba(230, 230, 230, 1)",
        rangeslider=dict(visible=False)),
    yaxis = dict(
        title=dict(text="QCH (LPH)", font=dict(size=12), standoff=0),
        showline=True, linewidth=1.2, linecolor="black",
        zeroline=True, zerolinewidth=1.2, zerolinecolor="rgba(230, 230, 230, 1)",
        showgrid=True, gridcolor="rgba(230, 230, 230, 1)"),
    legend = dict(x=1.02, y=0.5, orientation="v", bordercolor="black", borderwidth=0.5, font=dict(size=8)),
    plot_bgcolor="white", width=700, height=400)
fig.show()

QCH Analysis


# TCW Analysis
Load_Data_DF.loc[:, "TCWR"] = Load_Data_DF.loc[:, ["TCWR_1", "TCWR_2", "TCWR_3", "TCWR_4", "TCWR_5"]].mean(axis=1)
TCW_analysis = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["TCWS", "TCWR"]).describe().round(2)
print("Cooling Water Analysis")
display(TCW_analysis)

DF = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["TCWS", "TCWR"])
x_targ = DF.index
fig = go.Figure()
for Chiller_name, color_thick, color_thin in zip(Chiller_List, ["#2CBDFE", "#00cc99"], ["#80d6fe", "#b3ffec"]):
    try:
        y_targ1 = DF["TCWS"][Chiller_name]
        y_targ2 = DF["TCWR"][Chiller_name]
        fig.add_trace(go.Scatter(name=F"{Chiller_name}-TCWS", x=x_targ, y=y_targ1, mode="lines", line=dict(color=color_thick), showlegend=True))
        fig.add_trace(go.Scatter(name=F"{Chiller_name}-TCWR", x=x_targ, y=y_targ2, mode="lines", line=dict(color=color_thin), showlegend=True))
    except:
        print(F"{Chiller_name} Data is not sufficient to plot...\n")

fig.update_layout(
    title = dict(text=F"TCW Timeseries", x=0.04, y=0.83),
    xaxis = dict(
        showline=True, linewidth=1.2, linecolor="black",
        showticklabels=True, showgrid=True, gridcolor="rgba(230, 230, 230, 1)",
        rangeslider=dict(visible=False)),
    yaxis = dict(
        title=dict(text="Temp. (<sup>o</sup>C)", font=dict(size=12), standoff=0),
        showline=True, linewidth=1.2, linecolor="black",
        zeroline=True, zerolinewidth=1.2, zerolinecolor="rgba(230, 230, 230, 1)",
        showgrid=True, gridcolor="rgba(230, 230, 230, 1)"),
    legend = dict(x=1.02, y=0.5, orientation="v", bordercolor="black", borderwidth=0.5, font=dict(size=8)),
    plot_bgcolor="white", width=700, height=400)
fig.show()

Cooling Water Analysis


# RT Analysis
RT_analysis = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["RT_cal"]).describe().round(2)
print("Cooling Load Analysis")
display(RT_analysis)

DF = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["RT_cal"])
x_targ = DF.index
fig = go.Figure()
for Chiller_name, color_thick, color_thin in zip(Chiller_List, ["#2CBDFE", "#00cc99"], ["#80d6fe", "#b3ffec"]):
    try:
        y_targ1 = DF["RT_cal"][Chiller_name]
        fig.add_trace(go.Scatter(name=F"{Chiller_name}-RT", x=x_targ, y=y_targ1, mode="lines", line=dict(color=color_thick), showlegend=True))
    except:
        print(F"{Chiller_name} Data is not sufficient to plot...\n")

fig.update_layout(
    title = dict(text=F"Cooling Load Timeseries", x=0.04, y=0.83),
    xaxis = dict(
        showline=True, linewidth=1.2, linecolor="black",
        showticklabels=True, showgrid=True, gridcolor="rgba(230, 230, 230, 1)",
        rangeslider=dict(visible=False)),
    yaxis = dict(
        title=dict(text="Cooling Load (USRT)", font=dict(size=12), standoff=0),
        showline=True, linewidth=1.2, linecolor="black",
        zeroline=True, zerolinewidth=1.2, zerolinecolor="rgba(230, 230, 230, 1)",
        showgrid=True, gridcolor="rgba(230, 230, 230, 1)"),
    legend = dict(x=1.02, y=0.5, orientation="v", bordercolor="black", borderwidth=0.5, font=dict(size=8)),
    plot_bgcolor="white", width=700, height=400)
fig.show()

Cooling Load Analysis


# KWRT Analysis
KWRT_analysis = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["KWRT_cal"]).describe().round(2)
print("Chiller Performance Analysis")
display(KWRT_analysis)

DF = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["KWRT_cal"])
x_targ = DF.index
fig = go.Figure()
for Chiller_name, color_thick, color_thin in zip(Chiller_List, ["#2CBDFE", "#00cc99"], ["#80d6fe", "#b3ffec"]):
    try:
        y_targ1 = DF["KWRT_cal"][Chiller_name]
        fig.add_trace(go.Scatter(name=F"{Chiller_name}-KWRT", x=x_targ, y=y_targ1, mode="lines", line=dict(color=color_thick), showlegend=True))
    except:
        print(F"{Chiller_name} Data is not sufficient to plot...\n")

fig.update_layout(
    title = dict(text=F"Chiller Performance Timeseries", x=0.04, y=0.83),
    xaxis = dict(
        showline=True, linewidth=1.2, linecolor="black",
        showticklabels=True, showgrid=True, gridcolor="rgba(230, 230, 230, 1)",
        rangeslider=dict(visible=False)),
    yaxis = dict(
        title=dict(text="Chiller Performance (KW/RT)", font=dict(size=12), standoff=0),
        showline=True, linewidth=1.2, linecolor="black",
        zeroline=True, zerolinewidth=1.2, zerolinecolor="rgba(230, 230, 230, 1)",
        showgrid=True, gridcolor="rgba(230, 230, 230, 1)"),
    legend = dict(x=1.02, y=0.5, orientation="v", bordercolor="black", borderwidth=0.5, font=dict(size=8)),
    plot_bgcolor="white", width=700, height=400)
fig.show()

Chiller Performance Analysis

	id_point	building_id	last_updated	first_updated	device	objectId	name_point	description	units	tagged_units	...	measurement_id	datasource_hash	topic	equip_id	id_bdg	name_bdg	equip_type_abbr	equip_type_name	area_served_desc	tags
0	310029	441	1.560571e+12	1.550542e+12	NaN	NaN	245 E 149th St. Boiler-1 Fault Code	NaN	?	?	...	18	b115b5053493748f7076ad09f69c4456	nyserda+0000088737+boiler-1+245 E 149th St. Bo...	28797	441	88737	BLR	Boiler	NaN	['boiler', 'hvac']
1	310009	441	1.560485e+12	1.550701e+12	NaN	NaN	225 E 149th St. Boiler-1 Exhaust Temp	NaN	F	f	...	1	4a1a11502acee3078352da312c40c262	nyserda+0000088737+boiler-1+225 E 149th St. Bo...	28797	441	88737	BLR	Boiler	NaN	['boiler', 'hvac']
2	310010	441	1.560571e+12	1.550504e+12	NaN	NaN	225 E 149th St. Boiler-1 Fault Code	NaN	?	?	...	18	55882633fe8fb5aac7dac643c99b1af5	nyserda+0000088737+boiler-1+225 E 149th St. Bo...	28797	441	88737	BLR	Boiler	NaN	['boiler', 'hvac']
3	310011	441	1.560485e+12	1.550504e+12	NaN	NaN	225 E 149th St. Boiler-1 FFWD Temp	NaN	?	?	...	18	831a8df09b848f53e807fa0204c45eaa	nyserda+0000088737+boiler-1+225 E 149th St. Bo...	28797	441	88737	BLR	Boiler	NaN	['boiler', 'hvac']
4	310012	441	1.560485e+12	1.550504e+12	NaN	NaN	225 E 149th St. Boiler-1 Fire Rate In	NaN	?	?	...	18	aa147c829c7ed55b6e93cdbbb8b43d55	nyserda+0000088737+boiler-1+225 E 149th St. Bo...	28797	441	88737	BLR	Boiler	NaN	['boiler', 'hvac']

	id_point	building_id	name_point	description	units	tagged_units	equip_id	equip_type_abbr	equip_type_name	tags
5677	379788	488	dagPowerMeasurement_Chiller 1A	NaN	kw	kw	33139	CH	Chiller	['chiller', 'hvac']
5678	379787	488	dagChillerAmperageMeasurement_Chiller 1A	NaN	a	a	33139	CH	Chiller	['chiller', 'hvac']
5679	379786	488	dagChilledWaterSupplyMeasurement_Chiller 1A	NaN	f	f	33139	CH	Chiller	['chiller', 'hvac']
5680	379785	488	dagChilledWaterReturnMeasurement_Chiller 1A	NaN	f	f	33139	CH	Chiller	['chiller', 'hvac']
5681	379792	488	dagPowerMeasurement_Chiller 1B	NaN	kw	kw	33140	CH	Chiller	['chiller', 'hvac']
...	...	...	...	...	...	...	...	...	...	...
5779	379881	488	dagSupplyFanStatusMeasurement_Floors 23-41 Per...	NaN	on/off	on/off	33176	FAN	Fan	['fan', 'hvac', 'supplyFan']
5780	379880	488	dagSupplyAirFanSpeedMeasurement_Floors 23-41 P...	NaN	rpm	rpm	33176	FAN	Fan	['fan', 'hvac', 'supplyFan']
5781	379884	488	dagSupplyFanStatusMeasurement_Floors 42-49 Per...	NaN	on/off	on/off	33177	FAN	Fan	['fan', 'hvac', 'supplyFan']
5782	379883	488	dagSupplyAirFanSpeedMeasurement_Floors 42-49 P...	NaN	rpm	rpm	33177	FAN	Fan	['fan', 'hvac', 'supplyFan']
5783	379882	488	dagSupplyAirFanSpeedMeasurement_Floors 42-49 P...	NaN	%	%	33177	FAN	Fan	['fan', 'hvac', 'supplyFan']

	308439	308007	308008	308009	308011	307611	308010	307602	307608	307605	...	308573	308574	308575	308576	308577	308718	308717	308720	308719	308721
timestamp
2020-05-01 04:00:00+00:00	NaN	99.0	0	1	1	0.00	62.0	0	70.65	70.01	...	0	1	1	0	0	46.8	1.0	44.6	1.0	1
2020-05-01 04:15:00+00:00	NaN	100.0	0	1	1	0.00	62.0	0	70.88	70.08	...	0	1	1	0	0	46.8	1.0	44.6	1.0	1
2020-05-01 04:30:00+00:00	NaN	99.0	0	1	1	0.00	62.0	0	71.05	70.13	...	0	1	1	0	0	46.8	1.0	44.6	1.0	1
2020-05-01 04:45:00+00:00	NaN	100.0	0	1	1	0.00	62.0	0	71.20	70.17	...	0	1	1	0	0	46.8	1.0	44.6	1.0	1
2020-05-01 05:00:00+00:00	NaN	100.0	0	1	1	0.00	62.0	0	71.35	70.23	...	0	1	1	0	0	46.8	1.0	44.6	1.0	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2020-10-31 23:00:00+00:00	NaN	0.0	0	0	0	60.30	62.0	0	64.00	66.66	...	0	1	1	0	0	47.3	1.0	0.0	0.0	0
2020-10-31 23:15:00+00:00	NaN	0.0	0	0	0	60.38	62.0	0	66.43	66.61	...	0	1	1	0	0	47.3	1.0	0.0	0.0	0
2020-10-31 23:30:00+00:00	NaN	0.0	0	0	0	60.31	62.0	0	64.25	66.53	...	0	1	1	0	0	47.3	1.0	0.0	0.0	0
2020-10-31 23:45:00+00:00	NaN	0.0	0	0	0	60.49	62.0	0	64.51	66.61	...	0	1	1	0	0	47.3	1.0	0.0	0.0	0
2020-11-01 00:00:00+00:00	NaN	0.0	0	0	0	60.44	62.0	0	66.30	66.55	...	0	1	1	0	0	47.3	1.0	0.0	0.0	0

	CHP_22_3-CHP_KWH	CHP_22_3-CHP_A	CHP_22_3-CHP_KW	CHP_22_3-CHP_VFD	CHP_22_4-CHP_KWH	CHP_22_4-CHP_A	CHP_22_4-CHP_KW	CHP_22_4-CHP_VFD	CH_22_3-QCH	CH_22_3-CH_KWH	...	CH_22_4-TCWS	CH_22_4-TCHS	CH_22_4-TCHS_SP	CH_22_4-IPCT	CH_22_4-CH_KW	CT_1-TCWR_1	CT_2-TCWR_2	CT_3-TCWR_3	CT_4-TCWR_4	CT_5-TCWR_5
timestamp
2020-05-01 04:00:00+00:00	867.7	33.4	5.3	20.6	681.4	33.0	4.5	20.9	283.875	3539610.0	...	17.572222	13.694444	4.444444	0.0	0.0	13.777778	12.166667	12.277778	12.444444	12.333333
2020-05-01 04:15:00+00:00	867.7	33.4	4.9	20.6	681.4	33.2	4.4	20.9	283.875	3539610.0	...	17.466667	13.505556	4.444444	0.0	0.0	13.277778	12.055556	12.166667	12.333333	12.111111
2020-05-01 04:30:00+00:00	867.7	33.4	4.9	20.6	681.4	33.1	4.4	20.9	283.875	3539610.0	...	17.444444	13.511111	4.444444	0.0	0.0	13.000000	11.888889	12.111111	12.222222	12.000000
2020-05-01 04:45:00+00:00	867.7	33.4	5.1	20.6	681.4	33.1	4.3	20.9	283.875	3539610.0	...	17.372222	13.538889	4.444444	0.0	0.0	13.000000	11.944444	12.166667	12.222222	12.055556
2020-05-01 05:00:00+00:00	867.7	33.4	4.9	20.6	681.4	33.2	4.5	20.9	283.875	3539610.0	...	17.316667	13.511111	4.444444	0.0	0.0	13.055556	11.944444	12.166667	12.222222	12.000000

	CHP_KWH	CHP_A	CHP_KW	CHP_VFD	QCH	TCHR	TCWS	TCHS	TCHS_SP	IPCT	CH_KW	TCWR_1	TCWR_2	TCWR_3	TCWR_4	TCWR_5	TCHD_cal	RT_cal	KWRT_cal	COP_cal
timestamp
2020-05-01 11:00:00+00:00	867.7	36.2	10.6	27.4	488405.802	8.622222	23.922222	3.911111	4.444444	1086.01	579.57	13.166667	15.722222	18.944444	15.888889	18.333333	4.711111	760.890873	0.761699	4.615995
2020-05-01 11:15:00+00:00	867.7	36.2	10.0	27.4	469813.125	7.955556	23.505556	3.222222	4.444444	1042.88	557.03	13.222222	15.277778	19.055556	15.722222	18.388889	4.733333	735.377687	0.757475	4.641739
2020-05-01 11:30:00+00:00	867.7	36.0	10.5	27.4	479889.552	7.461111	21.405556	2.688889	4.444444	898.14	483.28	13.277778	15.555556	18.222222	18.388889	17.111111	4.772222	757.321291	0.638144	5.509729
2020-05-01 11:45:00+00:00	867.7	36.0	10.5	27.4	479181.000	7.088889	23.005556	2.627778	4.444444	830.20	456.86	13.444444	21.722222	18.333333	18.388889	21.833333	4.461111	706.904657	0.646282	5.440347
2020-05-01 12:00:00+00:00	867.7	36.0	9.6	27.4	470664.750	6.761111	25.183333	2.672222	4.444444	765.71	431.78	13.611111	23.333333	18.722222	18.333333	23.388889	4.088889	636.407363	0.678465	5.182288

	CHP_KWH	CHP_A	CHP_KW	CHP_VFD	QCH	TCHR	TCWS	TCHS	TCHS_SP	IPCT	CH_KW	TCWR_1	TCWR_2	TCWR_3	TCWR_4	TCWR_5	TCHD_cal	RT_cal	KWRT_cal	COP_cal
timestamp
2020-07-20 15:30:00+00:00	706.0	58.1	47.7	49.2	841830.177	9.672222	30.161111	5.850000	4.444444	898.24	483.32	23.166667	23.611111	23.222222	23.222222	23.777778	3.822222	1064.041670	0.454230	7.740566
2020-07-20 15:45:00+00:00	706.0	65.1	58.2	52.8	659298.552	10.372222	29.161111	5.938889	4.444444	776.45	435.95	23.166667	23.611111	23.111111	23.222222	23.833333	4.433333	966.564235	0.451031	7.795481
2020-07-20 16:00:00+00:00	706.0	44.7	25.0	39.3	485994.000	12.072222	29.100000	6.200000	4.444444	908.74	488.47	23.277778	23.666667	23.333333	23.277778	23.888889	5.872222	943.738349	0.517590	6.793015
2020-07-20 16:15:00+00:00	706.0	49.1	32.3	39.5	521903.052	12.188889	29.761111	6.133333	4.444444	921.81	495.15	23.500000	23.777778	23.611111	23.611111	24.166667	6.055556	1045.110095	0.473778	7.421200
2020-07-20 16:30:00+00:00	706.0	46.8	28.4	39.5	500471.625	11.933333	29.844444	5.827778	4.444444	936.06	502.43	23.722222	23.722222	23.833333	23.500000	23.944444	6.105556	1010.468687	0.497225	7.071250

	TCHR		TCHS
Chiller_Name	CH_22_3	CH_22_4	CH_22_3	CH_22_4
count	12542.00	578.00	12542.00	578.00
mean	9.89	11.05	5.74	5.73
std	1.65	0.66	1.09	0.23
min	4.84	9.51	2.29	4.87
25%	8.83	10.63	5.31	5.63
50%	10.07	11.06	5.66	5.72
75%	11.32	11.54	6.59	5.83
max	15.42	12.96	9.38	7.04

	QCH
Chiller_Name	CH_22_3	CH_22_4
count	12542.00	578.00
mean	724934.26	784500.48
std	137417.94	169188.01
min	421127.43	436456.68
25%	620266.88	778101.38
50%	731402.80	852688.96
75%	828488.05	904035.14
max	1196817.00	1081704.55

	TCWR		TCWS
Chiller_Name	CH_22_3	CH_22_4	CH_22_3	CH_22_4
count	12542.00	578.00	12542.00	578.00
mean	21.46	21.60	27.14	26.76
std	1.60	1.26	1.83	1.78
min	9.26	19.41	15.12	23.19
25%	20.71	20.62	25.96	25.38
50%	21.58	21.46	27.16	26.59
75%	22.41	22.69	28.42	28.05
max	26.50	24.51	33.26	31.88

	RT_cal
Chiller_Name	CH_22_3	CH_22_4
count	12542.00	578.00
mean	1014.86	1362.25
std	347.59	289.81
min	114.88	826.04
25%	720.33	1085.38
50%	959.34	1398.41
75%	1284.17	1589.03
max	2335.06	2132.13

	KWRT_cal
Chiller_Name	CH_22_3	CH_22_4
count	12542.00	578.00
mean	0.57	0.51
std	0.05	0.03
min	0.36	0.36
25%	0.54	0.49
50%	0.56	0.50
75%	0.59	0.53
max	1.68	0.62